Learn Data Science is this complete self-study course for absolute beginners. Data science is considered the “sexiest job of the 21st century.” You will learn the important elements of data science. You will be introduced to the principles, practices and tools that make data science a powerful medium for critical insight into business and research. You have a solid foundation for future learning and applications in your work. With data science you can do what you want to do, and do it even better. This course covers the basics of data science, data sourcing, coding, mathematics, and statistics.

Course created by Barton Poulson from datalab.cc.
Check out the datalab.cc YouTube channel: https://www.youtube.com/user/datalabcc
Check out more free data science courses at http://datalab.cc/

️ Course content ️
️ Part 1: Data Science: An Introduction: Foundations of Data Science
– Welcome (1.1)
– Demand for Data Science (2.1)
– The Data Science Venn Diagram (2.2)
– The Data Science trajectory (2.3)
– Roles in data science (2.4)
– Teams in Data Science (2.5)
– Big data (3.1)
– Coding (3.2)
– Statistics (3.3)
– Company information (3.4)
– Do no harm (4.1)
– Method overview (5.1)
– Sourcing overview (5.2)
– Coding overview (5.3)
– Math overview (5.4)
– Statistics overview (5.5)
– Machine Learning overview (5.6)
– Interpretability (6.1)
– Actionable insights (6.2)
– Presentation graphs (6.3)
– Reproducible research (6.4)
– Next steps (7.1)

️ Part 2: Data Sourcing: Foundations of Data Science (1:39:46)
– Welcome (1.1)
– Statistics (2.1)
– Accuracy (2.2)
– Social measurement context (2.3)
– Existing data (3.1)
– APIs (3.2)
– Scraping (3.3)
– New data (4.1)
– Interviews (4.2)
– Surveys (4.3)
– Card sorting (4.4)
– Laboratory experiments (4.5)
– A/B testing (4.6)
– Next steps (5.1)

️ Part 3: Coding (2:32:42)
– Welcome (1.1)
– Spreadsheets (2.1)
– Tableau Public (2.2)
-SPSS (2.3)
– JASP (2.4)
– Other software (2.5)
-HTML (3.1)
-XML (3.2)
-JSON (3.3)
-R (4.1)
-Python (4.2)
-SQL (4.3)
– C, C and Java (4.4)
– bash (4.5)
– Regex (5.1)
– Next steps (6.1)

️ Part 4: Math (4:01:09)
– Welcome (1.1)
– Elementary Algebra (2.1)
– Linear Algebra (2.2)
– Systems of linear equations (2.3)
– Calculation (2.4)
– Calculus & Optimization (2.5)
– Big O (3.1)
– Probability (3.2)

️ Part 5: Statistics (4:44:03)
– Welcome (1.1)
– Exploratory overview (2.1)
– Exploratory images (2.2)
– Exploratory statistics (2.3)
– Descriptive statistics (2.4)
– Inferential statistics (3.1)
– Hypothesis testing (3.2)
– Estimate (3.3)
– Estimates (4.1)
– Fit size (4.2)
– Function selection (4.3)
– Problems in modeling (4.4)
– Model validation (4.5)
– Do-it-yourself (4.6)
– Next step (5.1)

—

Learn to code for free and get a developer job: https://www.freecodecamp.org

Read hundreds of articles about programming: https://www.freecodecamp.org/news

Please take the opportunity to connect and share this video with your friends and family if you find it helpful.